Bilkent University EEE 485 Term Project Report
Members of the Group:
Ceyhun
Emre Öztürk/ Dep.: EE
Ömer
Musa Battal/ Dep.: EE
Project Phase:
3 (Last Phase)
Introduction:
In this project, we are implementing a house price predictor using several
machine learning approaches. We used Python programming environment to apply
machine learning. Our
dataset was constructed by extracting data from zingat.com and sahibinden.com. We used 3 different learning methods
that were shown in class to find predictions of house prices. These methods are
Linear Regression, K-means Clustering and SNN (Shallow Neural Network). We used
K-means clustering to divide house notices into the groups according to their
features. This
way, we had a purpose of getting rid of nonlinearities of our dataset. Then, we applied Linear Regression
separately in each cluster.
